Multimodal Emotion Recognition in Conversation (MERC) relies on integrating heterogeneous signals, yet real-world modality missingness frequently disrupts these systems. We contend that missingness is not merely a loss of data fidelity but a rupture of the fine-grained inter-modal causal chains essential for reasoning. Existing methods, which primarily focus on statistical reconstruction, often fail to bridge these logical gaps, effectively leaving semantic holes. To address this, we propose the Causal-Enhanced Mixture-of-Experts and Hypergraph Network (CaM-HG), employing a "restore-then-mine" paradigm. First, a Causal-Enhanced MoE module conditions experts on historical context to synthesize missing features that are both realistic and causally consistent, thereby patching the broken topology. Subsequently, an Asymmetric Causal Dynamic Hypergraph mines high-order correlations from the restored graph while enforcing strict temporal causality. Experiments on IEMOCAP, CMU-MOSI, and CMU-MOSEI show consistent improvements in terms of WAF1 and accuracy over strong baselines, e.g., surpassing SOTA benchmarks by 1.43% and 1.25% on IEMOCAP. The source code is included in the supplementary material.

Although there have been some works using prompt learning for the Aspect-based Sentiment Analysis(ABSA) tasks, their methods of prompt-tuning are simple and crude. Compared with vanilla fine-tuning methods, prompt learning intuitively bridges the objective form gap between pre-training and fine-tuning. Concretely, simply constructing prompt related to aspect words fails to fully exploit the potential of Pre-trained Language Models, and conducting more robust and professional prompt engineering for downstream tasks is a challenging problem that needs to be solved urgently. Therefore, in this paper, we propose a novel Syntax-aware Enhanced Prompt method (SynPrompt), which sufficiently mines the key syntactic information related to aspect words from the syntactic dependency tree. Additionally, to effectively harness the domain-specific knowledge embedded within PLMs for the ABSA tasks, we construct two adaptive prompt frameworks to enhance the perception ability of the above method. After conducting extensive experiments on three benchmark datasets, we have found that our method consistently achieves favorable results. These findings not only demonstrate the effectiveness and rationality of our proposed methods but also provide a powerful alternative to traditional prompt-tuning.